Cams are considered as one of the most important mechanical components that depends the contact action to do its job and suffer a lot of with drawbacks to be predicted and overcame in the design process. this work aims to investigate the induced cam contact and the maximum shear stress energy or (von misses) stresses during the course of action analytically using Hertz contact stress equation and the principal stress formulations to find the maximum stress value and its position beneath the contacting surfaces. The experimental investigation adopted two dimensions photoelastic technique to analyze cam stresses under a plane polarized light. The problem has been numerically simulated using Ansys software version 15 as FE

In this study, generation of elliptical gears with different teeth profiles of crowned involute, double circular arc (DCA), and combined (crowned involute with DCA) has been developed. The resulting mathematical equations have been computerized and feed to CNC end mill machine to manufacture elliptical gear models with different profiles. These models are investigated in plane polariscope to show the resulting stresses under certain load. Comparison of photo-elastic stress results shows that combined elliptical gears with DCA side as a loaded side have a minimum resulting contact stress with a reduction percentage of 40% compare with contact stresses in counterpart elliptical gear of involute profile (which is commonly u

Determining the aerodynamic characteristics of iced airfoil is an important step in aircraft design.  The goal of this work is to study experimentally and numerically an iced airfoil to assess the aerodynamic penalties associated with presence of ice on the airfoil surface. Three iced shapes were tested on NACA 0012 straight wing at zero and non-zero angles of attack, at Reynolds No. equal to (3.36*105). The 2-D steady state continuity and momentum equations have been solved utilizing finite volume method to analyze the turbulent flow over a clean and iced airfoil. The results show that the ice shapes affected the aerodynamic characteristics due to the change in airfoil shape. The experimental results show that the horn iced airfoil

This study was design to characterize the immune response in experimentally Pseudomonas aeruginosa mastitis mice treated probiotic bifidocin and cazacin of Bifidobacterium spp. and Lactobacillus casei. We quantified the level of the IFN-γ and TNF-α cytokines in blood by ELISA technique. IFN-γ level was significantly higher in infected group compared to control (340.21 ± 41.61, 8.45 ± 0.83 pg/ml, respectively). While the level of IFN-γ was significantly higher in mastitis mice than bifidocin and cazacin treated mice. Also, TNF-α level showed a significant increase in mastitis mice compared to controls (320.11±40.33, 8.45±0.83pg/ml, respectively). Among mastitis and bifidocin (9 and 18 mg/ml), cazacin (11 and 22 mg/ml) treate

In this study, Cr−Mo−N thin films with different Mo contents were synthesised via closed field unbalanced magnetron sputtering ion plating. The effects of Mo content on the microstructure, chemical bonding state, and optical properties of the prepared films were investigated by X-ray diffraction spectroscopy (XRD), X-ray photoelectron spectroscopy (XPS), field emission scanning electron microscopy, and ultraviolet-visible spectrophotometry. XRD results determined the face centered cubic (fcc) structure of pure CrN film. The incorporation of molybdenum (Mo) in the CrN matrix was confirmed by both XRD and XPS analyses. The CrMoN coatings demonstrate various polycrystalline phases including CrN, γ-Mo2N, Cr with oxides layers of MoO3, CrO3

The rapid rise in the use of artificially generated faces has significantly increased the risk of identity theft in biometric authentication systems. Modern facial recognition technologies are now vulnerable to sophisticated attacks using printed images, replayed videos, and highly realistic 3D masks. This creates an urgent need for advanced, reliable, and mobile-compatible fake face detection systems. Research indicates that while deep learning models have demonstrated strong performance in detecting artificially generated faces, deploying these models on consumer mobile devices remains challenging due to limitations in computing power, memory, privacy, and processing speed. This paper highlights several key challenges: (1) optimiz

The rapid rise in the use of artificially generated faces has significantly increased the risk of identity theft in biometric authentication systems. Modern facial recognition technologies are now vulnerable to sophisticated attacks using printed images, replayed videos, and highly realistic 3D masks. This creates an urgent need for advanced, reliable, and mobile-compatible fake face detection systems. Research indicates that while deep learning models have demonstrated strong performance in detecting artificially generated faces, deploying these models on consumer mobile devices remains challenging due to limitations in computing power, memory, privacy, and processing speed. This paper highlights several key challenges: (1) optimiz

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p